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Non-Convex Potential Games for Finding Global Solutions to Sensor Network Localization

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Non-Convex Potential Games for Finding Global Solutions to S...

European control Conference (ECC)

作者： Gehui Xu Guanpu Chen Yiguang Hong Baris Fidan Thomas Parisini Karl H. Johansson Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Electrical and Electronic Engineering Imperial College London London UK Department of Engineering and Architecture University of Trieste Trieste Italy

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Sensor network localization (SNL) problems require determining the physical coordinates of all sensors in a network. This process relies on the global coordinates of anchors and the available measurements between non-anchor and anchor nodes. Attributed to the intrinsic non-convexity, obtaining a globally optimal solution to SNL is challenging, as well as implementing corresponding algorithms. In this paper, we formulate a non-convex multi-player potential game for a generic SNL problem to investigate the identification condition of the global Nash equilibrium (NE) therein, where the global NE represents the global solution of SNL. We employ canonical duality theory to transform the non-convex game into a complementary dual problem. Then we develop a conjugation-based algorithm to compute the stationary points of the complementary dual problem. On this basis, we show an identification condition of the global NE: the stationary point of the proposed algorithm satisfies a duality relation. Finally, simulation results are provided to validate the effectiveness of the theoretical results.

关键词： Location awareness Coordinate measuring machines Simulation Current measurement Noise Europe Games

Stabilization of hyperbolic reaction-diffusion systems on directed networks through the generalized Routh-Hurwitz criterion for complex polynomials

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arXiv 2024年

作者： Muolo, Riccardo Hastir, Anthony Nakao, Hiroya Department of Systems and Control Engineering Tokyo Institute of Technology Japan Department of Mathematics and Computer Science Bergische Universität Wuppertal Germany Department of Mathematics and naXys Namur Institute for Complex Systems University of Namur Belgium

The study of dynamical systems on complex networks is of paramount importance in engineering, given that many natural and artificial systems find a natural embedding on discrete topologies. For instance, power grids, chemical reactors and the brain, to name a few, can be modeled through the network formalism by considering elementary units coupled via the links. In recent years, scholars have developed numerical and theoretical tools to study the stability of those coupled systems when subjected to perturbations. In such framework, it was found that asymmetric couplings enhance the possibilities for such systems to become unstable. Moreover, in this scenario the polynomials whose stability needs to be studied bear complex coefficients, which makes the analysis more difficult. In this work, we put to use a recent extension of the well-known Routh-Hurwitz stability criterion, allowing to treat the complex coefficient case. Then, using the Brusselator model as a case study, we discuss the stability conditions and the regions of parameters when the networked system remains stable. © 2024, CC BY.

关键词： Complex networks

Stabilization of hyperbolic reaction-diffusion systems on directed networks through the generalized Routh-Hurwitz criterion for complex polynomials

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Stabilization of hyperbolic reaction-diffusion systems on di...

SICE International Symposium on control systems (ISCS)

作者： Riccardo Muolo Anthony Hastir Hiroya Nakao Department of Systems and Control Engineering Tokyo Institute of Technology Japan Department of Mathematics and Computer Science Bergische Universität Wuppertal Germany Department of Mathematics and naXys Namur Institute for Complex Systems University of Namur Belgium

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control of Isolated AC Microgrids with Constant Power Loads: A Set Invariance Approach

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Control of Isolated AC Microgrids with Constant Power Loads:...

Mediterranean Conference on control and Automation (MED)

作者： Grigoris Michos George C. Konstantopoulos Paul A. Trodden Visakan Kadirkamanathan Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street Sheffield UK Department of Electrical and Computer Engineering University of Patras Rion Greece

This paper proposes a robust control scheme for isolated AC Microgrids, where each node is connected locally to a constant power load (CPL). Contrary to many approaches in the literature, we consider the explicit model of the inverter dynamics and separate the overall system into two parts; a nominal subsystem parametrized by a nominal load and an error subsystem describing the difference between the true and the nominal voltage, resulting from perturbations of the load demand. In the presented analysis, we investigate the non-linear structure of the CPL in order to analytically describe its geometric effect on the network dynamics. We exploit this information to propose mild conditions on the tuning parameters such that a positive invariant set for the error dynamics exists and the distance between the true and the nominal voltage trajectories is bounded at all times. We demonstrate the properties of the proposed control scheme in a simulated scenario.

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

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arXiv 2023年

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila Automatic Control and Systems Engineering The University of Sheffield United Kingdom Department of Electrical and Computer Engineering Rowan University United States Department of Computer Science University of York United Kingdom

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Risk-Averse Learning with Delayed feedback

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

In real-world scenarios, the impacts of decisions may not manifest immediately. Taking these delays into account facilitates accurate assessment and management of risk in real-world environments, thereby ensuring the efficacy of strategies. In this paper, we investigate risk-averse learning using Conditional Value at Risk (CVaR) as risk measure, while incorporating delayed feedback with unknown but bounded delays. We develop two risk-averse learning algorithms that rely on one-point and two-point zeroth-order optimization approaches, respectively. The regret achieved by the algorithms is analyzed in terms of the cumulative delay and the number of total samplings. The results suggest that the two-point risk-averse learning achieves a smaller regret bound than the one-point algorithm. Furthermore, the one-point risk-averse learning algorithm attains sublinear regret under certain delay conditions, and the two-point risk-averse learning algorithm achieves sublinear regret with minimal restrictions on the delay. We provide numerical experiments on a dynamic pricing problem to demonstrate the performance of the proposed algorithms. © 2024, CC0.

关键词： Adversarial machine learning

DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

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DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

Exploiting Hankel–Toeplitz Structures for Fast Computation of Kernel Precision Matrices

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arXiv 2024年

作者： Viset, Frida Kullberg, Anton Wesel, Frederiek Solin, Arno Delft Center for Systems and Control Delft University of Technology Netherlands Department of Electrical Engineering Linköping University Sweden Department of Computer Science Aalto University Finland

The Hilbert–space Gaussian Process (hgp) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (gp) inference by projecting the gp onto M basis functions. These properties result in a favorable data-independent O(M3) computational complexity during hyperparameter optimization but require a dominating one-time precomputation of the precision matrix costing O(NM2) operations. In this paper, we lower this dominating computational complexity to O(NM) with no additional approximations. We can do this because we realize that the precision matrix can be split into a sum of Hankel–Toeplitz matrices, each having O(M) unique entries. Based on this realization we propose computing only these unique entries at O(NM) costs. Further, we develop two theorems that prescribe sufficient conditions for the complexity reduction to hold generally for a wide range of other approximate gp models, such as the Variational Fourier Feature (vff) approach. The two theorems do this with no assumptions on the data and no additional approximations of the gp models themselves. Thus, our contribution provides a pure speed-up of several existing, widely used, gp approximations, without further approximations. © 2024, CC BY.

关键词： Gaussian distribution

Differentially Private Online Federated Learning with Correlated Noise

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arXiv 2024年

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (ϵ, δ)DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024, CC BY.

关键词： Federated learning